

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class        A__(a_							):
						"""simple docstring"""






						_A    :				int       					=			(DDIMParallelScheduler,)
						_A    :				Tuple       					=			(('''eta''', 0.0), ('''num_inference_steps''', 50))





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Optional[Any]:
									a_						:						Optional[Any] 							= {
									    """num_train_timesteps""": 1_000,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									    """clip_sample""": True,
									}

									config.update(**_lowercase    )
									return config





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> str:
									a_						:						Union[str, Any] 							= self.scheduler_classes[0]
									a_						:						int 							= self.get_scheduler_config(**_lowercase    )
									a_						:						List[str] 							= scheduler_class(**_lowercase    )

									a_							,					a_						:						int 							= 10, 0.0

									a_						:						str 							= self.dummy_model()
									a_						:						Union[str, Any] 							= self.dummy_sample_deter

									scheduler.set_timesteps(_lowercase    )

									for t in scheduler.timesteps:
												a_						:						str 							= model(_lowercase  ,      _lowercase    )
												a_						:						Union[str, Any] 							= scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    ).prev_sample

									return sample





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									for timesteps in [100, 500, 1_000]:
												self.check_over_configs(num_train_timesteps=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_lowercase    )

									a_						:						Any 							= self.scheduler_classes[0]
									a_						:						int 							= self.get_scheduler_config(steps_offset=1    )
									a_						:						Optional[int] 							= scheduler_class(**_lowercase    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps  ,      torch.LongTensor([801, 601, 401, 201, 1]    )    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,      [0.0_0_2, 0.0_2, 0.2, 2]    ):
												self.check_over_configs(beta_start=_lowercase  ,      beta_end=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> int:
									for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									self.check_over_configs(thresholding=_lowercase    )
									for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(
															    thresholding=_lowercase  ,      prediction_type=_lowercase  ,      sample_max_value=_lowercase  ,      )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									for t in [1, 10, 49]:
												self.check_over_forward(time_step=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> int:
									for t, num_inference_steps in zip([1, 10, 50]  ,      [10, 50, 500]    ):
												self.check_over_forward(time_step=_lowercase  ,      num_inference_steps=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> int:
									for t, eta in zip([1, 10, 49]  ,      [0.0, 0.5, 1.0]    ):
												self.check_over_forward(time_step=_lowercase  ,      eta=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						str 							= self.scheduler_classes[0]
									a_						:						Optional[Any] 							= self.get_scheduler_config()
									a_						:						Any 							= scheduler_class(**_lowercase    )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ,      0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(420  ,      400    ) - 0.1_4_7_7_1    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(980  ,      960    ) - 0.3_2_4_6_0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0  ,      0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487  ,      486    ) - 0.0_0_9_7_9    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999  ,      998    ) - 0.0_2    )    ) < 1e-5





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Any 							= self.scheduler_classes[0]
									a_						:						List[str] 							= self.get_scheduler_config()
									a_						:						List[Any] 							= scheduler_class(**_lowercase    )

									a_							,					a_						:						List[str] 							= 10, 0.0
									scheduler.set_timesteps(_lowercase    )

									a_						:						Any 							= self.dummy_model()
									a_						:						Any 							= self.dummy_sample_deter
									a_						:						int 							= self.dummy_sample_deter + 0.1
									a_						:						int 							= self.dummy_sample_deter - 0.1

									a_						:						Optional[int] 							= samplea.shape[0]
									a_						:						int 							= torch.stack([samplea, samplea, samplea]  ,      dim=0    )
									a_						:						Union[str, Any] 							= torch.arange(_lowercase    )[0:3, None].repeat(1  ,      _lowercase    )

									a_						:						Any 							= model(samples.flatten(0  ,      1    )  ,      timesteps.flatten(0  ,      1    )    )
									a_						:						List[Any] 							= scheduler.batch_step_no_noise(_lowercase  ,      timesteps.flatten(0  ,      1    )  ,      samples.flatten(0  ,      1    )  ,      _lowercase    )

									a_						:						Union[str, Any] 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						str 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 1_1_4_7.7_9_0_4    ) < 1e-2
									assert abs(result_mean.item() - 0.4_9_8_2    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						List[str] 							= self.full_loop()

									a_						:						List[str] 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						List[str] 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 1_7_2.0_0_6_7    ) < 1e-2
									assert abs(result_mean.item() - 0.2_2_3_9_6_7    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Tuple 							= self.full_loop(prediction_type="""v_prediction"""    )

									a_						:						Dict 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						Any 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 5_2.5_3_0_2    ) < 1e-2
									assert abs(result_mean.item() - 0.0_6_8_4    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> int:
									# We specify different beta, so that the first alpha is 0.99
									a_						:						int 							= self.full_loop(set_alpha_to_one=_lowercase  ,      beta_start=0.0_1    )
									a_						:						Dict 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						Optional[int] 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 1_4_9.8_2_9_5    ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_5_1    ) < 1e-3





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									# We specify different beta, so that the first alpha is 0.99
									a_						:						Any 							= self.full_loop(set_alpha_to_one=_lowercase  ,      beta_start=0.0_1    )
									a_						:						int 							= torch.sum(torch.abs(_lowercase    )    )
									a_						:						Optional[Any] 							= torch.mean(torch.abs(_lowercase    )    )

									assert abs(result_sum.item() - 1_4_9.0_7_8_4    ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_4_1    ) < 1e-3





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


